{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 基于卷积神经网络的 CIFAR10 识别"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "---"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 介绍"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "CIFAR10 是由 Hinton 的两个大弟子 Alex Krizhevsky、Ilya Sutskever 收集的一个用于普适物体识别的数据集。本实验将利用 PyTorch 建立一个卷积神经网络模型对 CIFAR10 中的数据集进行分类和识别。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 知识点"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "-  CIFAR10\n",
    "-  数据的预处理\n",
    "- 卷积神经网络的相关概念\n",
    "- 模型的搭建\n",
    "- 模型的训练\n",
    "- 模型的测试与应用"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "---"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 数据的预处理"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "CIFAR-10 数据集由 10 个类的 60000 个 32x32 的彩色图像组成，即每个类有 6000 个图像。数据如下所示：\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"400px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/8248989510cf9e954ce799cea89cfcd8-0/wm\n",
    "\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "从上图可以看到，这 10 个类分别是：飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。每个类存在 6000 张图像（其中5000 在训练集中，1000 在测试集中）。即训练集中的图像总数为 $5000\\times10 = 50000$ 张，测试数据集共有图像 10000 张。让我们先定义出这些类别的名字："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat',\r\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\r\n",
    "len(classes)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat',\r\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\r\n",
    "len(classes)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们的任务就是训练出一个较好的模型，使其能够对任意一张图像进行识别。换句话说，我们希望得到的模型为：将任意一张图像放入该模型中，该模型能够准确输出该图像所属的类别。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "首先，让我们来下载该数据集合。由于该数据集过大，如果线上直接下载该数据集的话，速度会很慢。因此，我们先将数据集上传到了蓝桥云课的云服务器中，我们直接从这上面下载 CIFAR-10 数据集。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# !wget -nc \"https://labfile.oss.aliyuncs.com/courses/2534/cifar-10-python.tar.gz\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "import os\r\n",
    "from pathlib import Path\r\n",
    "\r\n",
    "print('WD:',os.getcwd())\r\n",
    "# os.chdir()\r\n",
    "WD = Path().cwd()\r\n",
    "DATA_PATH = Path(r'C:\\files\\git_repository\\pytorch-learning\\datasets')\r\n",
    "print('PATH:',DATA_PATH)\r\n",
    "print('WD:',WD)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WD: c:\\files\\git_repository\\pytorch-learning\\2pytorch基础入门实战\\12基于卷积神经网络的 CIFAR10 识别\n",
      "PATH: C:\\files\\git_repository\\pytorch-learning\\datasets\n",
      "WD: c:\\files\\git_repository\\pytorch-learning\\2pytorch基础入门实战\\12基于卷积神经网络的 CIFAR10 识别\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在对数据进行读取之前，我们可以添加一些预处理操作。在 《数据的预处理》章节中，我们已经详细的阐述了 `torchvision.transforms.Compose()` 的使用方法。我们可以通过该函数定义一个数据处理的集合，专门用于数据的处理。这里，我们对下载的图像进行的数据操作有： Tensor 类型的转换 和 数据的标准化。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "import torch\r\n",
    "import torchvision\r\n",
    "import torchvision.transforms as transforms\r\n",
    "import numpy as np\r\n",
    "# 定义预处理列表\r\n",
    "transform = transforms.Compose(\r\n",
    "    [transforms.ToTensor(),\r\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\r\n",
    "\r\n",
    "# CIFAR10: 60000 张 32x32 大小的彩色图片，这些图片共分 10 类,每类有 6000 张图像\r\n",
    "# root:指定数据集所在位置\r\n",
    "# train=True：表示若本地已经存在，无需下载。若不存在，则下载\r\n",
    "# transform：预处理列表，这样就可以返回预处理后的数据集合\r\n",
    "train_dataset = torchvision.datasets.CIFAR10(root=str(DATA_PATH), train=True,\r\n",
    "                                             download=False, transform=transform)\r\n",
    "\r\n",
    "test_dataset = torchvision.datasets.CIFAR10(root=str(DATA_PATH), train=False,\r\n",
    "                                            download=False, transform=transform)\r\n",
    "print(\"训练集的图像数量为：\", len(train_dataset))\r\n",
    "print(\"测试集的图像数量为\", len(test_dataset))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "训练集的图像数量为： 50000\n",
      "测试集的图像数量为 10000\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "import torch\r\n",
    "import torchvision\r\n",
    "import torchvision.transforms as transforms\r\n",
    "import numpy as np\r\n",
    "\r\n",
    "# 定义transform，加载数据集\r\n",
    "transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])\r\n",
    "train_dataset = torchvision.datasets.CIFAR10(root=str(DATA_PATH),train=True,download=False,transform=transform)\r\n",
    "train_dataset = torchvision.datasets.CIFAR10(root=str(DATA_PATH),train=False,download=False,transform=transform)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# 显示图片\r\n",
    "import matplotlib.pylab as plt\r\n",
    "import numpy as np\r\n",
    "import seaborn as sns\r\n",
    "\r\n",
    "iter_train = iter(train_dataset)\r\n",
    "for i in range(9):\r\n",
    "    x,y = next(iter_train)\r\n",
    "    plt.subplot(3,3,i+1).set_title(str(classes[y]))\r\n",
    "    # 这里是通道的改变，不能用reshape,应该用transpose\r\n",
    "    # x = np.array(x).reshape(32,32,3)\r\n",
    "    x = np.array(x).transpose(1,2,0)\r\n",
    "    plt.imshow(x)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "上面得到的数据集就是标准化后的 Tensor 数据。`torchvision.datasets.CIFAR10` 在运行时，会查找 `root` 目录下是否存在所需要的数据集合。如果存在，则直接加载。如果不存在， PyTorch 会从官网上下载该数据集合。由于官网属于外网，因此直接从官网下载数据集合的速度是非常慢的。这也就是为什么我们选择从蓝桥云课的云服务器中下载数据的原因。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "得到数据集后，我们就可以利用 ` torch.utils.data.DataLoader` 将数据集包装成一个数据生成器："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "batch_size = 4  # 设置批次个数\r\n",
    "# shuffle=True:表示加载数据前，会先打乱数据，提高模型的稳健性\r\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,\r\n",
    "                                           shuffle=True)\r\n",
    "\r\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,\r\n",
    "                                          shuffle=False)\r\n",
    "test_loader, test_loader"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x1b44758c0d0>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x1b44758c0d0>)"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# 包装成数据生成器\r\n",
    "batch_size = 256\r\n",
    "train_dataloader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True)\r\n",
    "test_dataloader = torch.utils.data.DataLoader(test_dataset,batch_size=2*batch_size,shuffle=False)\r\n",
    "\r\n",
    "test_dataloader,train_dataloader"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x1b44758ca90>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x1b44758ca60>)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，我们就可以利用已经定义好的数据加载器，加载几张图片，观察一下图片的具体效果："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "\r\n",
    "def imshow(img):\r\n",
    "    # 由于加载器产生的图片是归一化后的图片，因此这里需要将图片反归一化\r\n",
    "    # 变成归一化前的图像\r\n",
    "    img = img / 2 + 0.5\r\n",
    "    # 将图像从 Tensor 转为 NumPy\r\n",
    "    npimg = img.numpy()\r\n",
    "    # 产生的数据为 C×W×H 而 plt 展示的图像一般都是 W×H×C\r\n",
    "    # 因此，这里会有一个维度的变换\r\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\r\n",
    "    plt.show()\r\n",
    "\r\n",
    "\r\n",
    "# 随机获得一些训练图像\r\n",
    "dataiter = iter(train_loader)\r\n",
    "images, labels = dataiter.next()\r\n",
    "\r\n",
    "# 将这些图像进行展示\r\n",
    "imshow(torchvision.utils.make_grid(images))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "你可以多次运行上面代码，观察数据集中的图像。由于这些图像只有 $32×32$ 的大小，因此我们会感觉这些图像分辨率不是很高。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "其实这种大小的图像是非常好的，因为这种小图像在保存原有内容的条件下，能够尽可能的加快模型的训练速度（图像越大，建立的神经网络就会越大，模型训练的时间就会越长）。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 模型的建立"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# 我将使用pytorch-lightning来进行一次端到端的的训练和测试\r\n",
    "import torch\r\n",
    "import torch.nn as nn\r\n",
    "import torch.nn.functional as F\r\n",
    "import pytorch_lightning as pl\r\n",
    "\r\n",
    "class Net(pl.LightningModule):\r\n",
    "    def __init__(self):\r\n",
    "        super(Net, self).__init__()\r\n",
    "        self.conv1 = nn.Conv2d(3,6,5)\r\n",
    "        self.pool = nn.MaxPool2d(2,2)\r\n",
    "        self.conv2 = nn.Conv2d(6,16,5)\r\n",
    "        self.fc1 = nn.Linear(16*5*5,120)\r\n",
    "        self.fc2 = nn.Linear(120,84)\r\n",
    "        self.fc3 = nn.Linear(84,10)\r\n",
    "        # 最后接softmax输出用crossentropy输出\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        # 卷积过relu然后池化\r\n",
    "        x = self.pool(F.relu(self.conv1(x)))\r\n",
    "        # 卷积过relu然后池化\r\n",
    "        x = self.pool(F.relu(self.conv2(x)))\r\n",
    "        # 展平，fc1接受16*5*5\r\n",
    "        x = x.view(-1,16*5*5)\r\n",
    "        x = F.relu(self.fc1(x))\r\n",
    "        x = F.relu(self.fc2(x))\r\n",
    "        x = self.fc3(x)\r\n",
    "        return x\r\n",
    "    def configure_optimizers(self):\r\n",
    "        optimizer = torch.optim.Adam(self.parameters(),lr=1e-3)\r\n",
    "\r\n",
    "\r\n",
    "    def training_step(self,batch,batch_idx):\r\n",
    "        x,y = batch\r\n",
    "        y_pred = self.forward(x)\r\n",
    "        loss = nn.CrossEntropyLoss()(y_pred,y)\r\n",
    "        self.log('train_loss',loss)\r\n",
    "        return loss\r\n",
    "    \r\n",
    "    def test_step(self,batch,batch_idx):\r\n",
    "        x,y = batch\r\n",
    "        y_pred = self.forward(x)\r\n",
    "        loss = nn.CrossEntropyLoss()(y_pred,y)\r\n",
    "        self.log('test_loss',loss)\r\n",
    "    \r\n",
    "    \r\n",
    "\r\n",
    "\r\n",
    "net = Net()\r\n",
    "net\r\n",
    "# test_in = torch.randn(10,3,32,32)\r\n",
    "# print(net.forward(test_in))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Net(\n",
       "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
       "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "net = Net()\r\n",
    "trainer = pl.Trainer(max_epochs=20, progress_bar_refresh_rate=20)\r\n",
    "\r\n",
    "# Train the model ⚡\r\n",
    "trainer.fit(net,train_dataloaders=train_dataloader)\r\n",
    "\r\n",
    "# %%\r\n",
    "trainer.test(test_dataloaders=test_dataloader)\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\optimizers.py:36: UserWarning: `LightningModule.configure_optimizers` returned `None`, this fit will run with no optimizer\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name  | Type      | Params\n",
      "------------------------------------\n",
      "0 | conv1 | Conv2d    | 456   \n",
      "1 | pool  | MaxPool2d | 0     \n",
      "2 | conv2 | Conv2d    | 2.4 K \n",
      "3 | fc1   | Linear    | 48.1 K\n",
      "4 | fc2   | Linear    | 10.2 K\n",
      "5 | fc3   | Linear    | 850   \n",
      "------------------------------------\n",
      "62.0 K    Trainable params\n",
      "0         Non-trainable params\n",
      "62.0 K    Total params\n",
      "0.248     Total estimated model params size (MB)\n",
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\data_loading.py:105: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\data_loading.py:322: UserWarning: The number of training samples (40) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 0:   0%|          | 0/40 [00:00<00:00, 999.83it/s]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\torch\\nn\\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ..\\c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 19: 100%|██████████| 40/40 [00:03<00:00, 11.60it/s, loss=2.3, v_num=1]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:679: LightningDeprecationWarning: `trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.test(dataloaders)` instead.\n",
      "  rank_zero_deprecation(\n",
      "C:\\Users\\22459\\Miniconda3\\envs\\pytorch\\lib\\site-packages\\pytorch_lightning\\trainer\\data_loading.py:105: UserWarning: The dataloader, test dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Testing: 100%|██████████| 20/20 [00:02<00:00,  7.52it/s]--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_loss': 2.3041443824768066}\n",
      "--------------------------------------------------------------------------------\n",
      "Testing: 100%|██████████| 20/20 [00:02<00:00,  7.51it/s]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'test_loss': 2.3041443824768066}]"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "此次我们将使用卷积神经网络对 CIFAR10 进行识别。除了线性连接层、激活函数层外，卷积神经网络比全连接网络还多了卷积层和池化层。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**卷积运算** 其实属于分析数学中的一种运算方式。卷积层的主要目的就是对图像进行卷积进而达到提取图像特征的效果。针对于图像的特征提取，一般使用卷积而不是全连接的原因是，卷积的特征共享原则可以很好的解决全连接所带来的参数冗余问题。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "卷积过程其实就是将滤波器当做滑动窗口，然后对整个图像进行滑动计算。以下图为例子："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"600px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/2d45bd9ab97d86382245fa0b3f2039e1-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "卷积就是卷积核不停地对原图层的一部分进行卷积运算，上图即为一个 $4\\times4$ 的输入数据与一个 $3 \\times 3$ 的卷积核，以每次滑动一个方格为步长的卷积操作。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在 PyTorch 中，我们可以使用 `nn.Conv2d(input_channel, out_channel, fileter_size)` 进行卷积操作。其中："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "- input_channel：表示输入图层的通道数。\n",
    "- out_channel：表示输出图层的通道数。\n",
    "- fileter_size：表示过滤器的大小($fileter\\_size\\times fileter\\_size$)。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**池化层** 主要用于对数据和参数量的压缩。池化操作的过程其实就是把输入的图像划分成多个矩形区域，每个区域进行一次池化，得到一个值。最后，将每个区域输出的值进行排列得到最终的池化结果。如下图所示："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"400px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/b4ed247dd7b350c460b2acf01cc35497-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "池化操作有很多种，其中最常用的池化操作就是最大池化和平均池化。而上图阐述的就是最大池化操作。将输入图层放入了一个 的最大池化层中，然后输入图层被分割成了 4 个区域，取每个区域内的最大值作为该区域的输出值，再把这些值排列起来，形成池化结果。同理，平均池化就是输出每个区域中的平均值。池化操作可以去除图中的冗余信息，有效的防止模型的过拟合。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "在 PyTorch 中，我们可以使用 `torch.nn.MaxPool2d(kernel_size, stride)` 来进行最大池化的操作。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "- kernel_size：滤波器的大小\n",
    "- stride：步长"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "为了能够识别 CIFAR 数据集，我们将利用 PyTorch 建立一个能够对 CIFAR 数据集进行分类的网络模型。该网络模型结构如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<img width=\"300px\" src=\"https://doc.shiyanlou.com/courses/2534/1166617/6e67afb279739202d384519d87071356-0/wm\">"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "其中 conv1 和 conv2 是卷积核大小为 $5\\times5$ 的卷积层。当然，这两个卷积层的输入和输出是不同的。fc1、fc2、fc3 为三个全连接层。conv1 和 conv2 之间存在一个池化层。conv2 和 fc1 之间存在一个池化层。这些卷积层、池化层和全连接层之间都有一个 激活函数层。一般我们常说的神经网络层其实是网络层加激活函数层。因此，这里我们并没有将激活函数层画出来，不过在代码实现时，应当有激活函数层。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "让我们利用 PyTorch 代码来对其进行实现："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "import torch.nn.functional as F\r\n",
    "import torch.nn as nn\r\n",
    "\r\n",
    "# 网络模型的建立\r\n",
    "\r\n",
    "\r\n",
    "class ConvNet(nn.Module):\r\n",
    "    def __init__(self):\r\n",
    "        super(ConvNet, self).__init__()\r\n",
    "        # 神经网络的输入为 三个通道\r\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\r\n",
    "        self.pool = nn.MaxPool2d(2, 2)\r\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\r\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\r\n",
    "        self.fc2 = nn.Linear(120, 84)\r\n",
    "        # 由于一共有 10 个类，因此模型的输出节点数量为 10\r\n",
    "        self.fc3 = nn.Linear(84, 10)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        # -> n, 3, 32, 32\r\n",
    "        # 传入数据，且为输出数据添加激活函数\r\n",
    "        x = self.pool(F.relu(self.conv1(x)))  # -> n, 6, 14, 14\r\n",
    "        x = self.pool(F.relu(self.conv2(x)))  # -> n, 16, 5, 5\r\n",
    "        x = x.view(-1, 16 * 5 * 5)            # -> n, 400\r\n",
    "        x = F.relu(self.fc1(x))               # -> n, 120\r\n",
    "        x = F.relu(self.fc2(x))               # -> n, 84\r\n",
    "        x = self.fc3(x)                       # -> n, 10\r\n",
    "        return x\r\n",
    "\r\n",
    "\r\n",
    "# 定义当前设备是否支持 GPU\r\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n",
    "model = ConvNet().to(device)\r\n",
    "model"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "ConvNet(\n",
       "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
       "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
       "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
       "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "建立完模型后，接下来，让我们对损失函数和优化器进行定义。这里我们使用交叉熵作为模型的损失函数，使用 SGD 算法作为梯度下降的优化器。代码如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "learning_rate = 0.001\r\n",
    "criterion = nn.CrossEntropyLoss()\r\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\r\n",
    "criterion, optimizer"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(CrossEntropyLoss(),\n",
       " SGD (\n",
       " Parameter Group 0\n",
       "     dampening: 0\n",
       "     lr: 0.001\n",
       "     momentum: 0\n",
       "     nesterov: False\n",
       "     weight_decay: 0\n",
       " ))"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "上面代码的结果可以看出，我们所编写的模型结构和上面的图像一致。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 模型的训练"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "模型训练的步骤是固定的，我们只需要在编写时，注意模型的输入即可。如果是全连接神经网络，我们需要将图片转为一个行向量，即每行代表一条数据。如果是卷积神经网络，我们就可以直接将图片作为输入。当然，在输入时，我们也需要注意模型的输入大小和图片的大小是否一致。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "模型训练的步骤和上一章节一致，如下："
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "- 通过模型的正向传播，输出预测结果。\n",
    "- 通过预测结果和真实标签计算损失。\n",
    "- 通过后向传播，获得梯度。\n",
    "- 通过梯度更新模型的权重。\n",
    "- 进行梯度的清空。\n",
    "- 循环上面操作，直到损失较小为止。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "由于这里使用的是 CPU 运行，因此训练速度较慢。下面代码可能会运行 15~20 min ，请耐心等待："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "num_epochs = 5\r\n",
    "# 定义数据长度\r\n",
    "n_total_steps = len(train_loader)\r\n",
    "print(\"Start training....\")\r\n",
    "for epoch in range(num_epochs):\r\n",
    "    for i, (images, labels) in enumerate(train_loader):\r\n",
    "        # 原始数据集的大小，每个批次的大小为: [4, 3, 32, 32]\r\n",
    "        # 将数据转为模型支持的环境类型。\r\n",
    "        images = images.to(device)\r\n",
    "        labels = labels.to(device)\r\n",
    "\r\n",
    "        # 模型的正向传播，得到数据数据的预测值\r\n",
    "        outputs = model(images)\r\n",
    "        # 根据预测值计算损失\r\n",
    "        loss = criterion(outputs, labels)\r\n",
    "\r\n",
    "        # 固定步骤：梯度清空、反向传播、参数更新\r\n",
    "        optimizer.zero_grad()\r\n",
    "        loss.backward()\r\n",
    "        optimizer.step()\r\n",
    "\r\n",
    "        if (i+1) % 2000 == 0:\r\n",
    "            print(\r\n",
    "                f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')\r\n",
    "\r\n",
    "\r\n",
    "print('Finished Training')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start training....\n",
      "Epoch [1/5], Step [2000/2500], Loss: 2.3434\n",
      "Epoch [2/5], Step [2000/2500], Loss: 2.3065\n",
      "Epoch [3/5], Step [2000/2500], Loss: 2.2759\n",
      "Epoch [4/5], Step [2000/2500], Loss: 1.9646\n"
     ]
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "为了保证训练模型在后面的使用，在完成模型的训练后，我们一般都会将模型持久化，即保存在指定目录下，方便下次直接加载。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们可以通过 `model.state_dict(modl,PATH)` 来将指定模型 model 保存到 PATH 中："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "PATH = './cnn.pth'\r\n",
    "torch.save(model.state_dict(), PATH)\r\n",
    "print(\"The model have been saved！\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 模型的测试"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，让我们加载本地已经保存好的模型，进行模型的测试。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "由于我们这里的模型训练和测试是一气呵成的，因此内存中存在已训练的模型。但是为了讲解模型加载的知识点，这里我们还是从本地读取刚才训练好的模型。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们可以通过 `model.load_state_dict(torch.load(PATH))`  来加载本地的模型："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "new_model = ConvNet()\r\n",
    "new_model.load_state_dict(torch.load(PATH))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "接下来，我们将利用测试集来计算模型的总识别准确率以及每一类图像的识别准确率："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "with torch.no_grad():\r\n",
    "    # 统计预测正确的图像数量和进行了预测的图像数量\r\n",
    "    n_correct = 0\r\n",
    "    n_samples = 0\r\n",
    "    # 统计每类图像中，预测正确的图像数量和该类图像的实际数量\r\n",
    "    n_class_correct = [0 for i in range(10)]\r\n",
    "    n_class_samples = [0 for i in range(10)]\r\n",
    "    for images, labels in test_loader:\r\n",
    "        images = images.to(device)\r\n",
    "        labels = labels.to(device)\r\n",
    "        outputs = new_model(images)\r\n",
    "        # 利用 max 函数返回 10 个类别中概率最大的下标，即预测的类别\r\n",
    "        _, predicted = torch.max(outputs, 1)\r\n",
    "        n_samples += labels.size(0)\r\n",
    "        # 通过判断预测值和真实标签是否相同，来统计预测正确的样本数\r\n",
    "        n_correct += (predicted == labels).sum().item()\r\n",
    "        # 计算每种种类的预测正确数\r\n",
    "        for i in range(batch_size):\r\n",
    "            label = labels[i]\r\n",
    "            pred = predicted[i]\r\n",
    "            if (label == pred):\r\n",
    "                n_class_correct[label] += 1\r\n",
    "            n_class_samples[label] += 1\r\n",
    "    # 输出总的模型准确率\r\n",
    "    acc = 100.0 * n_correct / n_samples\r\n",
    "    print(f'Accuracy of the network: {acc} %')\r\n",
    "\r\n",
    "    # 输出每个类别的模型准确率\r\n",
    "    for i in range(10):\r\n",
    "        acc = 100.0 * n_class_correct[i] / n_class_samples[i]\r\n",
    "        print(f'Accuracy of {classes[i]}: {acc} %')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "从结果可以看出，我们建立的模型的识别准确率不是很高。这其实是多方面的原因，首先是我们建立的卷积神经网络模型还不够深，没有提取到更多有用的图像特征。其次，是我们迭代的次数还不够多，损失还没有降到最低。当然，如果仅仅是为了了解 PyTorch 的具体用法，上面的模型已经足够。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "如果需要一个能够具有更高识别率的模型，我们就需要建立一个更深的神经网络，VGG16，用于数据集合的识别。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### VGG16 模型"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "通过下面代码，我们可以知道 VGG16 看起来非常复杂的。但仔细观察，该模型和上面模型的组件相同。即都是由卷积层、全连接层、激活函数、池化层等组成的。下面代码，无需手敲，直接运行即可。当然，如果你想学的更加深入，建议还是自己推敲一下，下面代码为 VGG16 的网络结构。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "import torch.nn.functional as F\r\n",
    "import torch.nn as nn\r\n",
    "\r\n",
    "# 网络模型的建立\r\n",
    "\r\n",
    "\r\n",
    "class VGG16(nn.Module):\r\n",
    "    def __init__(self, num_classes=10):\r\n",
    "        super(VGG16, self).__init__()\r\n",
    "        self.features = nn.Sequential(\r\n",
    "            # 1\r\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(64),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 2\r\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(64),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\r\n",
    "            # 3\r\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(128),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 4\r\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(128),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\r\n",
    "            # 5\r\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(256),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 6\r\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(256),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 7\r\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(256),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\r\n",
    "            # 8\r\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 9\r\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 10\r\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\r\n",
    "            # 11\r\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 12\r\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            # 13\r\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\r\n",
    "            nn.BatchNorm2d(512),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\r\n",
    "            nn.AvgPool2d(kernel_size=1, stride=1),\r\n",
    "        )\r\n",
    "        self.classifier = nn.Sequential(\r\n",
    "            # 14\r\n",
    "            nn.Linear(512, 4096),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.Dropout(),\r\n",
    "            # 15\r\n",
    "            nn.Linear(4096, 4096),\r\n",
    "            nn.ReLU(True),\r\n",
    "            nn.Dropout(),\r\n",
    "            # 16\r\n",
    "            nn.Linear(4096, num_classes),\r\n",
    "        )\r\n",
    "        #self.classifier = nn.Linear(512, 10)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        out = self.features(x)\r\n",
    "        out = out.view(out.size(0), -1)\r\n",
    "        out = self.classifier(out)\r\n",
    "        return out\r\n",
    "\r\n",
    "\r\n",
    "# 定义当前设备是否支持 GPU\r\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n",
    "model = VGG16().to(device)\r\n",
    "model"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "同理，我们在模型训练时，也需要增加迭代次数，将迭代次数增加到 20。由于线上环境只存在 CPU，且利用 CPU 训练一个深度神经网络是非常缓慢的。因此，这里我不再对上面的神经网络进行训练。你可以通过点击 [这里](https://www.kaggle.com/tianyanxiaobai/pytorch-14-cifar10?scriptVersionId=37596531) 访问我在 Kaggle 上搭建的完整项目代码和 GPU 运行的项目结果。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "现在，让我们直接将 GPU 运行的模型下载到本地："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "!wget -nc \"https://labfile.oss.aliyuncs.com/courses/2534/vggcnn.pth\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "让我们利用 `model.load_state_dict(torch.load(PATH)) ` 加载已经训练好的模型。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "注意：由于线上环境支持 CPU，而已加载的模型是在 GPU 上训练的 。因此，在读取模型时，我们需要添加参数 `map_location='cpu'`:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "new_model = VGG16().to(device)\r\n",
    "# 在CPU 环境下加载 GPU 运行的模型时，我们需要添加参数 `map_location='cpu'`\r\n",
    "new_model.load_state_dict(torch.load(\"vggcnn.pth\", map_location='cpu'))\r\n",
    "print(\"已加载模型\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "和上面的模型测试一样，让我们将测试数据放入模型中，计算 VGG 模型的识别准确率（由于 VGG16 网络很深，因此测试需要一定时间，大约 3~5 min）："
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# 重新下设置 batch_size，使模型一次能够预测更多的数据\r\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=125,\r\n",
    "                                          shuffle=False)\r\n",
    "with torch.no_grad():\r\n",
    "    # 统计预测正确的图像数量和进行了预测的图像数量\r\n",
    "    n_correct = 0\r\n",
    "    n_samples = 0\r\n",
    "    i = 0\r\n",
    "    for images, labels in test_loader:\r\n",
    "        images = images.to(device)\r\n",
    "        labels = labels.to(device)\r\n",
    "        outputs = new_model(images)\r\n",
    "        # 利用 max 函数返回 10 个类别中概率最大的下标，即预测的类别\r\n",
    "        _, predicted = torch.max(outputs, 1)\r\n",
    "        n_samples += labels.size(0)\r\n",
    "        # 通过判断预测值和真实标签是否相同，来统计预测正确的样本数\r\n",
    "        n_correct += (predicted == labels).sum().item()\r\n",
    "        if i % 10 == 0:\r\n",
    "            print(\"已预测完第{}批次的数据\".format(i))\r\n",
    "        i = i+1\r\n",
    "\r\n",
    "    # 输出总的模型准确率\r\n",
    "    acc = 100.0 * n_correct / n_samples\r\n",
    "    print(f'VGG模型的识别准确率为: {acc} %')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "可以看到，仅仅是迭代了 20 次，VGG16 模型的识别准确率就可以达到 78% 左右。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "由此可以得到：网络模型的结构越深，得到的细节越多，对物体的识别能力越强。当然，世界上没有免费的午餐。随着网络模型的加深，我们的训练难度和训练成本也会提高。因此，如何在模型的识别准确率和训练时间之间寻找一个平衡是研究界的一个重要方向。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 实验总结"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "本实验首先介绍了 CIFAR10 数据集，然后利用相关函数对其进行了预处理操作。然后，根据卷积神经网络的相关知识，利用 PyTorch 建立了相关的神经网络模型。接着，定义了损失和优化器，对模型进行了训练。最后将提前分割出来的测试集放入模型进行测试，得到已训练模型的识别准确率。"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "<hr><div style=\"color: #999; font-size: 12px;\"><i class=\"fa fa-copyright\" aria-hidden=\"true\"> 本课程内容版权归蓝桥云课所有，禁止转载、下载及非法传播。</i></div>"
   ],
   "metadata": {}
  }
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